Overview

Dataset statistics

Number of variables9
Number of observations239880
Missing cells481711
Missing cells (%)22.3%
Duplicate rows13227
Duplicate rows (%)5.5%
Total size in memory18.3 MiB
Average record size in memory80.0 B

Variable types

Numeric9

Alerts

Dataset has 13227 (5.5%) duplicate rowsDuplicates
wime_komfort is highly overall correlated with wime_sauberkeit and 2 other fieldsHigh correlation
wime_sauberkeit is highly overall correlated with wime_komfort and 1 other fieldsHigh correlation
wime_platzangebot is highly overall correlated with wime_komfort and 2 other fieldsHigh correlation
wime_gesamtzuf is highly overall correlated with wime_komfort and 1 other fieldsHigh correlation
wime_personal has 156813 (65.4%) missing valuesMissing
wime_komfort has 53029 (22.1%) missing valuesMissing
wime_sauberkeit has 50033 (20.9%) missing valuesMissing
wime_puenktlich has 49390 (20.6%) missing valuesMissing
wime_platzangebot has 48565 (20.2%) missing valuesMissing
wime_gesamtzuf has 41342 (17.2%) missing valuesMissing
wime_preis_leistung has 15990 (6.7%) missing valuesMissing
wime_fahrplan has 8751 (3.6%) missing valuesMissing
wime_oes_fahrt has 57798 (24.1%) missing valuesMissing
wime_komfort has 3037 (1.3%) zerosZeros
wime_puenktlich has 4923 (2.1%) zerosZeros
wime_platzangebot has 6693 (2.8%) zerosZeros
wime_preis_leistung has 6777 (2.8%) zerosZeros
wime_fahrplan has 5635 (2.3%) zerosZeros

Reproduction

Analysis started2023-01-26 13:47:13.285416
Analysis finished2023-01-26 13:47:46.977681
Duration33.69 seconds
Software versionpandas-profiling vv3.6.2
Download configurationconfig.json

Variables

wime_personal
Real number (ℝ)

Distinct13
Distinct (%)< 0.1%
Missing156813
Missing (%)65.4%
Infinite0
Infinite (%)0.0%
Mean89.978471
Minimum0
Maximum100
Zeros721
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size3.7 MiB
2023-01-26T14:47:47.106692image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile50
Q177.777778
median100
Q3100
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)22.222222

Descriptive statistics

Standard deviation17.72018
Coefficient of variation (CV)0.19693799
Kurtosis6.7746899
Mean89.978471
Median Absolute Deviation (MAD)0
Skewness-2.364068
Sum7474241.7
Variance314.00477
MonotonicityNot monotonic
2023-01-26T14:47:47.357225image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
100 54883
 
22.9%
75 10661
 
4.4%
88.88888889 5367
 
2.2%
77.77777778 4839
 
2.0%
50 1920
 
0.8%
66.66666667 1877
 
0.8%
44.44444444 885
 
0.4%
55.55555556 817
 
0.3%
0 721
 
0.3%
25 468
 
0.2%
Other values (3) 629
 
0.3%
(Missing) 156813
65.4%
ValueCountFrequency (%)
0 721
 
0.3%
11.11111111 138
 
0.1%
22.22222222 227
 
0.1%
25 468
 
0.2%
33.33333333 264
 
0.1%
44.44444444 885
 
0.4%
50 1920
 
0.8%
55.55555556 817
 
0.3%
66.66666667 1877
 
0.8%
75 10661
4.4%
ValueCountFrequency (%)
100 54883
22.9%
88.88888889 5367
 
2.2%
77.77777778 4839
 
2.0%
75 10661
 
4.4%
66.66666667 1877
 
0.8%
55.55555556 817
 
0.3%
50 1920
 
0.8%
44.44444444 885
 
0.4%
33.33333333 264
 
0.1%
25 468
 
0.2%

wime_komfort
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct13
Distinct (%)< 0.1%
Missing53029
Missing (%)22.1%
Infinite0
Infinite (%)0.0%
Mean78.993675
Minimum0
Maximum100
Zeros3037
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size3.7 MiB
2023-01-26T14:47:47.599961image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile33.333333
Q175
median77.777778
Q3100
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)25

Descriptive statistics

Standard deviation22.75024
Coefficient of variation (CV)0.28800078
Kurtosis1.4869674
Mean78.993675
Median Absolute Deviation (MAD)22.222222
Skewness-1.2398074
Sum14760047
Variance517.57343
MonotonicityNot monotonic
2023-01-26T14:47:47.846754image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
100 71729
29.9%
75 40929
17.1%
77.77777778 17438
 
7.3%
88.88888889 12623
 
5.3%
50 11304
 
4.7%
66.66666667 10817
 
4.5%
55.55555556 5996
 
2.5%
44.44444444 4845
 
2.0%
25 3090
 
1.3%
0 3037
 
1.3%
Other values (3) 5043
 
2.1%
(Missing) 53029
22.1%
ValueCountFrequency (%)
0 3037
 
1.3%
11.11111111 1002
 
0.4%
22.22222222 1685
 
0.7%
25 3090
 
1.3%
33.33333333 2356
 
1.0%
44.44444444 4845
 
2.0%
50 11304
 
4.7%
55.55555556 5996
 
2.5%
66.66666667 10817
 
4.5%
75 40929
17.1%
ValueCountFrequency (%)
100 71729
29.9%
88.88888889 12623
 
5.3%
77.77777778 17438
 
7.3%
75 40929
17.1%
66.66666667 10817
 
4.5%
55.55555556 5996
 
2.5%
50 11304
 
4.7%
44.44444444 4845
 
2.0%
33.33333333 2356
 
1.0%
25 3090
 
1.3%

wime_sauberkeit
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct13
Distinct (%)< 0.1%
Missing50033
Missing (%)20.9%
Infinite0
Infinite (%)0.0%
Mean79.210906
Minimum0
Maximum100
Zeros1709
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size3.7 MiB
2023-01-26T14:47:48.101032image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile44.444444
Q175
median77.777778
Q3100
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)25

Descriptive statistics

Standard deviation21.484636
Coefficient of variation (CV)0.27123331
Kurtosis1.1261964
Mean79.210906
Median Absolute Deviation (MAD)22.222222
Skewness-1.0950981
Sum15037953
Variance461.5896
MonotonicityNot monotonic
2023-01-26T14:47:48.341193image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
100 70179
29.3%
75 43668
18.2%
77.77777778 18011
 
7.5%
50 14096
 
5.9%
88.88888889 13642
 
5.7%
66.66666667 10775
 
4.5%
55.55555556 5581
 
2.3%
44.44444444 4508
 
1.9%
25 3587
 
1.5%
33.33333333 2167
 
0.9%
Other values (3) 3633
 
1.5%
(Missing) 50033
20.9%
ValueCountFrequency (%)
0 1709
 
0.7%
11.11111111 606
 
0.3%
22.22222222 1318
 
0.5%
25 3587
 
1.5%
33.33333333 2167
 
0.9%
44.44444444 4508
 
1.9%
50 14096
 
5.9%
55.55555556 5581
 
2.3%
66.66666667 10775
 
4.5%
75 43668
18.2%
ValueCountFrequency (%)
100 70179
29.3%
88.88888889 13642
 
5.7%
77.77777778 18011
 
7.5%
75 43668
18.2%
66.66666667 10775
 
4.5%
55.55555556 5581
 
2.3%
50 14096
 
5.9%
44.44444444 4508
 
1.9%
33.33333333 2167
 
0.9%
25 3587
 
1.5%

wime_puenktlich
Real number (ℝ)

MISSING  ZEROS 

Distinct13
Distinct (%)< 0.1%
Missing49390
Missing (%)20.6%
Infinite0
Infinite (%)0.0%
Mean88.854446
Minimum0
Maximum100
Zeros4923
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size3.7 MiB
2023-01-26T14:47:48.596801image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile33.333333
Q188.888889
median100
Q3100
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)11.111111

Descriptive statistics

Standard deviation22.192639
Coefficient of variation (CV)0.24976397
Kurtosis6.0885439
Mean88.854446
Median Absolute Deviation (MAD)0
Skewness-2.4966627
Sum16925883
Variance492.51324
MonotonicityNot monotonic
2023-01-26T14:47:48.848303image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
100 131734
54.9%
75 19244
 
8.0%
88.88888889 11702
 
4.9%
77.77777778 7321
 
3.1%
0 4923
 
2.1%
50 4593
 
1.9%
66.66666667 2952
 
1.2%
25 2360
 
1.0%
44.44444444 1566
 
0.7%
55.55555556 1485
 
0.6%
Other values (3) 2610
 
1.1%
(Missing) 49390
 
20.6%
ValueCountFrequency (%)
0 4923
 
2.1%
11.11111111 629
 
0.3%
22.22222222 988
 
0.4%
25 2360
 
1.0%
33.33333333 993
 
0.4%
44.44444444 1566
 
0.7%
50 4593
 
1.9%
55.55555556 1485
 
0.6%
66.66666667 2952
 
1.2%
75 19244
8.0%
ValueCountFrequency (%)
100 131734
54.9%
88.88888889 11702
 
4.9%
77.77777778 7321
 
3.1%
75 19244
 
8.0%
66.66666667 2952
 
1.2%
55.55555556 1485
 
0.6%
50 4593
 
1.9%
44.44444444 1566
 
0.7%
33.33333333 993
 
0.4%
25 2360
 
1.0%

wime_platzangebot
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct13
Distinct (%)< 0.1%
Missing48565
Missing (%)20.2%
Infinite0
Infinite (%)0.0%
Mean80.111828
Minimum0
Maximum100
Zeros6693
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size3.7 MiB
2023-01-26T14:47:49.098209image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile22.222222
Q175
median100
Q3100
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)25

Descriptive statistics

Standard deviation26.782767
Coefficient of variation (CV)0.33431726
Kurtosis1.3450694
Mean80.111828
Median Absolute Deviation (MAD)0
Skewness-1.4404719
Sum15326594
Variance717.31661
MonotonicityNot monotonic
2023-01-26T14:47:49.337870image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
100 96385
40.2%
75 28630
 
11.9%
77.77777778 12081
 
5.0%
50 10872
 
4.5%
88.88888889 10341
 
4.3%
66.66666667 6876
 
2.9%
0 6693
 
2.8%
25 5100
 
2.1%
55.55555556 4096
 
1.7%
44.44444444 3937
 
1.6%
Other values (3) 6304
 
2.6%
(Missing) 48565
20.2%
ValueCountFrequency (%)
0 6693
 
2.8%
11.11111111 1549
 
0.6%
22.22222222 2316
 
1.0%
25 5100
 
2.1%
33.33333333 2439
 
1.0%
44.44444444 3937
 
1.6%
50 10872
 
4.5%
55.55555556 4096
 
1.7%
66.66666667 6876
 
2.9%
75 28630
11.9%
ValueCountFrequency (%)
100 96385
40.2%
88.88888889 10341
 
4.3%
77.77777778 12081
 
5.0%
75 28630
 
11.9%
66.66666667 6876
 
2.9%
55.55555556 4096
 
1.7%
50 10872
 
4.5%
44.44444444 3937
 
1.6%
33.33333333 2439
 
1.0%
25 5100
 
2.1%

wime_gesamtzuf
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct13
Distinct (%)< 0.1%
Missing41342
Missing (%)17.2%
Infinite0
Infinite (%)0.0%
Mean84.600079
Minimum0
Maximum100
Zeros2142
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size3.7 MiB
2023-01-26T14:47:49.586919image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile50
Q175
median88.888889
Q3100
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)25

Descriptive statistics

Standard deviation19.562327
Coefficient of variation (CV)0.23123296
Kurtosis3.7252961
Mean84.600079
Median Absolute Deviation (MAD)11.111111
Skewness-1.7165337
Sum16796331
Variance382.68462
MonotonicityNot monotonic
2023-01-26T14:47:49.824410image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
100 93341
38.9%
75 41884
17.5%
88.88888889 20359
 
8.5%
77.77777778 16801
 
7.0%
50 7774
 
3.2%
66.66666667 6768
 
2.8%
55.55555556 2801
 
1.2%
44.44444444 2196
 
0.9%
0 2142
 
0.9%
25 2035
 
0.8%
Other values (3) 2437
 
1.0%
(Missing) 41342
17.2%
ValueCountFrequency (%)
0 2142
 
0.9%
11.11111111 475
 
0.2%
22.22222222 915
 
0.4%
25 2035
 
0.8%
33.33333333 1047
 
0.4%
44.44444444 2196
 
0.9%
50 7774
 
3.2%
55.55555556 2801
 
1.2%
66.66666667 6768
 
2.8%
75 41884
17.5%
ValueCountFrequency (%)
100 93341
38.9%
88.88888889 20359
 
8.5%
77.77777778 16801
 
7.0%
75 41884
17.5%
66.66666667 6768
 
2.8%
55.55555556 2801
 
1.2%
50 7774
 
3.2%
44.44444444 2196
 
0.9%
33.33333333 1047
 
0.4%
25 2035
 
0.8%

wime_preis_leistung
Real number (ℝ)

MISSING  ZEROS 

Distinct13
Distinct (%)< 0.1%
Missing15990
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean73.836296
Minimum0
Maximum100
Zeros6777
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size3.7 MiB
2023-01-26T14:47:50.064576image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile25
Q150
median75
Q3100
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)50

Descriptive statistics

Standard deviation26.38286
Coefficient of variation (CV)0.3573156
Kurtosis0.23451501
Mean73.836296
Median Absolute Deviation (MAD)25
Skewness-0.91729285
Sum16531208
Variance696.05532
MonotonicityNot monotonic
2023-01-26T14:47:50.297364image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
100 79727
33.2%
75 49854
20.8%
50 27847
 
11.6%
77.77777778 13293
 
5.5%
66.66666667 10076
 
4.2%
25 9107
 
3.8%
88.88888889 7804
 
3.3%
0 6777
 
2.8%
55.55555556 6284
 
2.6%
44.44444444 6253
 
2.6%
Other values (3) 6868
 
2.9%
(Missing) 15990
 
6.7%
ValueCountFrequency (%)
0 6777
 
2.8%
11.11111111 1219
 
0.5%
22.22222222 2588
 
1.1%
25 9107
 
3.8%
33.33333333 3061
 
1.3%
44.44444444 6253
 
2.6%
50 27847
11.6%
55.55555556 6284
 
2.6%
66.66666667 10076
 
4.2%
75 49854
20.8%
ValueCountFrequency (%)
100 79727
33.2%
88.88888889 7804
 
3.3%
77.77777778 13293
 
5.5%
75 49854
20.8%
66.66666667 10076
 
4.2%
55.55555556 6284
 
2.6%
50 27847
 
11.6%
44.44444444 6253
 
2.6%
33.33333333 3061
 
1.3%
25 9107
 
3.8%

wime_fahrplan
Real number (ℝ)

MISSING  ZEROS 

Distinct13
Distinct (%)< 0.1%
Missing8751
Missing (%)3.6%
Infinite0
Infinite (%)0.0%
Mean83.524617
Minimum0
Maximum100
Zeros5635
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size3.7 MiB
2023-01-26T14:47:50.564079image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile25
Q175
median100
Q3100
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)25

Descriptive statistics

Standard deviation23.909205
Coefficient of variation (CV)0.28625339
Kurtosis2.5885985
Mean83.524617
Median Absolute Deviation (MAD)0
Skewness-1.7015161
Sum19304961
Variance571.65007
MonotonicityNot monotonic
2023-01-26T14:47:50.833282image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
100 126720
52.8%
75 35535
 
14.8%
77.77777778 14678
 
6.1%
88.88888889 12666
 
5.3%
50 11455
 
4.8%
66.66666667 7748
 
3.2%
0 5635
 
2.3%
25 4731
 
2.0%
55.55555556 3956
 
1.6%
44.44444444 3745
 
1.6%
Other values (3) 4260
 
1.8%
(Missing) 8751
 
3.6%
ValueCountFrequency (%)
0 5635
 
2.3%
11.11111111 840
 
0.4%
22.22222222 1509
 
0.6%
25 4731
 
2.0%
33.33333333 1911
 
0.8%
44.44444444 3745
 
1.6%
50 11455
 
4.8%
55.55555556 3956
 
1.6%
66.66666667 7748
 
3.2%
75 35535
14.8%
ValueCountFrequency (%)
100 126720
52.8%
88.88888889 12666
 
5.3%
77.77777778 14678
 
6.1%
75 35535
 
14.8%
66.66666667 7748
 
3.2%
55.55555556 3956
 
1.6%
50 11455
 
4.8%
44.44444444 3745
 
1.6%
33.33333333 1911
 
0.8%
25 4731
 
2.0%

wime_oes_fahrt
Real number (ℝ)

Distinct13
Distinct (%)< 0.1%
Missing57798
Missing (%)24.1%
Infinite0
Infinite (%)0.0%
Mean90.706965
Minimum0
Maximum100
Zeros423
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size3.7 MiB
2023-01-26T14:47:51.090497image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile66.666667
Q177.777778
median100
Q3100
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)22.222222

Descriptive statistics

Standard deviation14.743643
Coefficient of variation (CV)0.16254147
Kurtosis5.5206182
Mean90.706965
Median Absolute Deviation (MAD)0
Skewness-2.0032814
Sum16516106
Variance217.37501
MonotonicityNot monotonic
2023-01-26T14:47:51.334373image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
100 114608
47.8%
75 25758
 
10.7%
88.88888889 16983
 
7.1%
77.77777778 12813
 
5.3%
66.66666667 4225
 
1.8%
50 3582
 
1.5%
55.55555556 1485
 
0.6%
44.44444444 943
 
0.4%
25 632
 
0.3%
0 423
 
0.2%
Other values (3) 630
 
0.3%
(Missing) 57798
24.1%
ValueCountFrequency (%)
0 423
 
0.2%
11.11111111 106
 
< 0.1%
22.22222222 210
 
0.1%
25 632
 
0.3%
33.33333333 314
 
0.1%
44.44444444 943
 
0.4%
50 3582
 
1.5%
55.55555556 1485
 
0.6%
66.66666667 4225
 
1.8%
75 25758
10.7%
ValueCountFrequency (%)
100 114608
47.8%
88.88888889 16983
 
7.1%
77.77777778 12813
 
5.3%
75 25758
 
10.7%
66.66666667 4225
 
1.8%
55.55555556 1485
 
0.6%
50 3582
 
1.5%
44.44444444 943
 
0.4%
33.33333333 314
 
0.1%
25 632
 
0.3%

Interactions

2023-01-26T14:47:41.295652image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T14:47:16.541064image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T14:47:19.487753image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T14:47:22.550859image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T14:47:25.852247image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T14:47:29.058740image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T14:47:32.526524image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T14:47:35.740003image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T14:47:38.523136image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T14:47:41.601725image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T14:47:16.909111image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T14:47:19.863131image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T14:47:22.911496image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T14:47:26.230970image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T14:47:29.418673image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T14:47:33.029918image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T14:47:36.053256image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T14:47:38.832684image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T14:47:41.906685image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T14:47:17.239336image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T14:47:20.207907image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T14:47:23.291446image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T14:47:26.594768image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T14:47:29.759999image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T14:47:33.400353image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T14:47:36.363566image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T14:47:39.145537image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T14:47:42.213877image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T14:47:17.577294image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T14:47:20.551532image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T14:47:23.631047image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T14:47:26.960076image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T14:47:30.105545image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T14:47:33.840481image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T14:47:36.668095image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T14:47:39.453403image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T14:47:42.541336image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T14:47:17.895573image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T14:47:20.896693image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T14:47:23.976001image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T14:47:27.320629image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T14:47:30.441684image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T14:47:34.172887image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T14:47:36.977611image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T14:47:39.763551image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T14:47:42.840458image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T14:47:18.196100image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T14:47:21.230073image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T14:47:24.298108image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T14:47:27.672169image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T14:47:30.787337image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T14:47:34.504719image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T14:47:37.287702image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T14:47:40.072158image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T14:47:43.150497image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T14:47:18.524972image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T14:47:21.553971image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T14:47:24.664730image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T14:47:28.024362image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T14:47:31.144180image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T14:47:34.843243image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T14:47:37.602365image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T14:47:40.388672image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T14:47:43.464929image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T14:47:18.848811image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T14:47:21.884736image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T14:47:25.039305image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T14:47:28.362075image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T14:47:31.497577image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T14:47:35.152338image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T14:47:37.921681image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T14:47:40.704925image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T14:47:43.772630image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T14:47:19.151163image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T14:47:22.217817image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T14:47:25.518244image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T14:47:28.700623image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T14:47:31.851983image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T14:47:35.454820image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T14:47:38.224206image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-26T14:47:41.006856image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-01-26T14:47:51.579871image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
wime_personalwime_komfortwime_sauberkeitwime_puenktlichwime_platzangebotwime_gesamtzufwime_preis_leistungwime_fahrplanwime_oes_fahrt
wime_personal1.0000.4310.4120.4160.4400.4710.3500.4130.359
wime_komfort0.4311.0000.6310.3630.5490.5530.4010.3930.355
wime_sauberkeit0.4120.6311.0000.3340.5270.5000.3500.3350.373
wime_puenktlich0.4160.3630.3341.0000.3700.4570.2940.4200.305
wime_platzangebot0.4400.5490.5270.3701.0000.5190.3780.3620.312
wime_gesamtzuf0.4710.5530.5000.4570.5191.0000.4980.4900.430
wime_preis_leistung0.3500.4010.3500.2940.3780.4981.0000.4380.259
wime_fahrplan0.4130.3930.3350.4200.3620.4900.4381.0000.293
wime_oes_fahrt0.3590.3550.3730.3050.3120.4300.2590.2931.000

Missing values

2023-01-26T14:47:44.138399image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-01-26T14:47:44.832469image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-01-26T14:47:46.425040image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

wime_personalwime_komfortwime_sauberkeitwime_puenktlichwime_platzangebotwime_gesamtzufwime_preis_leistungwime_fahrplanwime_oes_fahrt
239965NaN100.0100.0100.050.0100.075.0100.0100.0
239976NaNNaNNaNNaNNaNNaN50.0100.0NaN
239979NaNNaNNaNNaNNaNNaNNaNNaNNaN
239954NaNNaNNaNNaNNaNNaNNaNNaNNaN
239988100.0100.0100.0100.050.0100.0100.0100.0100.0
240020NaN75.075.0100.075.075.050.075.075.0
240019NaN25.0NaN100.075.075.0100.0100.0100.0
240016NaNNaN75.075.050.075.050.0100.0100.0
239966NaNNaNNaNNaNNaNNaNNaNNaNNaN
239983100.075.050.0100.050.0100.050.075.0100.0
wime_personalwime_komfortwime_sauberkeitwime_puenktlichwime_platzangebotwime_gesamtzufwime_preis_leistungwime_fahrplanwime_oes_fahrt
880100.077.77777855.555556100.000000100.00000077.77777877.777778100.000000100.000000
881NaN100.00000077.777778100.00000077.77777888.88888933.33333344.444444100.000000
882NaN77.77777866.666667100.00000066.66666777.77777888.88888977.77777877.777778
883100.088.88888977.777778100.00000088.88888988.88888988.88888988.88888988.888889
884NaN66.666667100.000000100.000000100.00000088.88888966.666667100.000000100.000000
885NaN100.000000100.000000100.000000100.000000100.000000100.000000100.000000100.000000
886100.022.22222255.55555611.1111110.00000033.3333330.00000022.22222244.444444
887NaN77.77777855.555556100.000000100.00000077.7777780.000000100.000000100.000000
888NaN66.66666777.777778100.00000066.66666777.77777855.55555655.55555666.666667
913NaN100.000000100.00000088.888889100.00000088.888889100.000000100.000000100.000000

Duplicate rows

Most frequently occurring

wime_personalwime_komfortwime_sauberkeitwime_puenktlichwime_platzangebotwime_gesamtzufwime_preis_leistungwime_fahrplanwime_oes_fahrt# duplicates
12574NaN100.0100.0100.0100.0100.0100.0100.0100.011606
13215NaNNaNNaNNaNNaNNaN100.0100.0NaN10650
5137100.0100.0100.0100.0100.0100.0100.0100.0100.08967
13226NaNNaNNaNNaNNaNNaNNaNNaNNaN7249
13181NaNNaNNaNNaNNaNNaN75.0100.0NaN5145
13180NaNNaNNaNNaNNaNNaN75.075.0NaN3453
13153NaNNaNNaNNaNNaNNaN50.0100.0NaN2517
13092NaNNaNNaNNaNNaN100.0100.0100.0NaN2268
12502NaN100.0100.0100.0100.0100.075.0100.0100.02105
5078100.0100.0100.0100.0100.0100.075.0100.0100.02047